Healthcare is teeming with data. We have all witnessed pictures of ICUs with multiple devices scrutinizing critical statistics, displaying readings, and chiming warnings. But what we often overlook is that data that rolls off the mesh and is not collared. If you’re acquainted or aware of data analytics, you would be able to apprehend the value that comes from collaring, marking, and interpreting that information.
I had the amicable opportunity to converse with Dr. Emma Fauss, Cofounder and CEO of Medical Informatics Corp., and discuss how capturing and analyzing this data has transformed healthcare and how this changeover has evolved specifically consequential during a global pandemic. To know the bits and bytes of our conversation related to data analytics in healthcare industry, make sure you listen to our podcast via the link shared below.
A couple of decades back, Emma cofounded MIC with Dr. Craig Rusin and formed their Sickbay solution. Sickbay permits users to draw together the previously deleted and lost data and leverage it to furnish patient administration functionality and administrative advancements and enhance the patient’s medical journal.
There’s presumably existed a requirement for analytics in the healthcare industry for ages, but it has been technologically challenging to organize and hold the data at such a high scale. Further, individuals have loomed the crisis by pouring to acquire as much information as feasible in any possible form or manner without truly comprehending the end-use matter. Previous endeavors have taken one machine and assembled one dashboard for that device. Following this reason, you end up with multiple machines right next to the bedside, all attached to different dashboards, which wasn’t scalable.
MIC germinated from an investigation ground with a specific service case. If they could identify the routine on the mesh for witnessing circumstances, they could determine those sub-acute customs in the betterment and provide healthcare experts early alarm. The significance begins to rise exponentially when you merge the data across machines.
MIC was presented to Intel via another technology counterpart in 2019 and is ecstatic to partner with such a symbiotic ally. Emma’s knowledge as an engineer implies she always had a fondness towards Intel and their mastership to “assemble items in the ranking hierarchy of nanometers”. Intel assisted MIC to open a Blue Ocean of untried types of data analytics that could be scaled up, deployed, commercialized, and suggested that prototypes could be devised in a matter of span.
Rather than spending decades on developing a medical gimmick, they can, in periodic months, conceive and construct a prototype that can be utilized in real-time. Intel also enabled its protection schemes and looked at methods to enforce federated data standards.
When the pandemic transpired, the Intel team was keen to provide the necessary support and glimpsed an opportunity to team up with MIC.
Bryce Olson, the Global Strategist for the Intel Health and Life Sciences Group, visited healthcare providers, questioning what they required when feedback concerning their misery pinpoints instantly led to the Sickbay answer.
Here is a few feedback that included in the hospital responses:
Infirmaries were swamped with patients and laboured to mount their team to conform to patients’ requirements.
Restricted stockpiles of Personal Protective Equipment (PPE) implied capping staff interactions with patients.
Because COVID19 is extremely infectious, medical staffers who were straight in contact with patients were at elevated risk, and if they contracted the virus, that indicated more irregular personnel are obtainable to minister patients.
During this pandemic period, time was of the substance. Infirmaries required devices to quickly obtain all their proprietary patient health records from disparate bedside machines into one screened vista.
In connection to Intel’s $50 million pandemic retort, Intel and MIC established the Scale to Serve Program to support 100 infirmaries continually installing MIC’s Sickbay outlet. Some of the remarkable attributes of Sickbay comprise:
Sickbay is the sole scalable, FDA-cleared clinical administration and analytics outlet developed for ICUs.
The Scale to Serve Program enables infirmaries to remotely observe ICU patients across agents, departments, and establishments, so infirmary staff can look after more patients at once while lowering their peril of exposure to COVID19.
It employs Intel Xeon processor-based outlets to furnish data visualization and analytics and can be attained by healthcare workers operating any affiliated desktop, notepad, or mobile device.
There wasn’t a moment where the MIC team said, “do we do this or not?”. Emma stated it was pleasing to observe how engineers and science coupled up to resolve things.
Acquiring an alliance behind such a modification can be an arduous job from a logistics and planning, administration, and technology standpoint. Because of the product’s path and scalability, it’s clear that the development can be nestled in a day. But it’s all the additional elements across an institution that must be dragged concurrently. Nevertheless, the skillfulness and scalability of the answer surprised consumers.
Emma says they desired to drive an accessible medical device integration. “You shouldn’t have to set a completely fresh set of hardware in the space when you’ve already subsidized a lot in your web structure”.
Medics can scrutinize the crazes and the real-time data in healthcare industry of the patient and provide sustenance direction remotely without undervaluing the grade of care. Consolidating immediate contact also diminishes the drain on lacking aids such as PPE.
The secondary advantage is that you can leverage caregivers at abodes and those under quarantine. Moreover, you can access caregivers who are potentially in other states, which provides tremendous flexibility as we see the strain on health care aids ebb and course across provinces with multiple tides of the COVID19 pandemic.
The threat reduction by utilizing virtual ICU technology is extensive.
Today a very small percentage of data developed by patients is driven into the electronic health record, compelling medics to function without having the complete picture. Sickbay opens that data to permit predictive analytics, AI and machine learning applications, remote patient monitoring, eventually enabling providers to devise a new care benchmark.
COVID19 has consolidated the instantaneous requirement to decrease dealings in space and scrutinize aid restrictions differently. Scanning more patients with minimal face-to-face dealings without renouncing the grade of care, surveying numerous patients at one time, and leveraging data for the patient course, analytics, and threat scoring.
The Sickbay forum was approximately before the pandemic, but its components distinctively assisted the requirements of this global crisis by permitting infirmaries to handle those challenges.
One of the greatest understandings has been that once an institution has a scalable structure, it can acclimate to a spectrum of diverse scenarios in a matter of days/hours. Therefore, it places institutions to deal with pandemics and further scenarios that might come into space. And by employing predictive analytics and vacating the obligation of management, the solution enables it to bear a favourable influence on the care crew, the patient, and the lowest line.
I want to show my gratitude to Emma, her team, and the entire crew of Intel for gathering together to make this interview a success. Please tune in to the discussion at the link mentioned below and employ the additional aids to comprehend our discussed technology and innovations more.
Rebounding for the data-driven fate with FPGA and eASICS
Data is now the new driving force of the modern world. How well your business performs or what its rank could be on the performance list entirely depends on how you leverage the data available, involving emerging tools such as ML, AI, and cloud! Such forces have, in turn, lit up a stand for Field Programmable Gate Arrays (FPGAs).
Lately, I have had a great fortune to sit down and have an in-depth discussion with Jim Dworkin, the senior director of the cloud business unit in the Programmable Solutions Group at Intel. During our discussion, even Jim asserted that in order to unlock the potential of data, we need to embrace the latest FPGA technology.
Further, having the modern architecture out into the right place now could easily uncover the paths to get things right. Perhaps, we need to educate ourselves on the ‘hardwiring’ of data flow to ensure we can appropriately leverage the power of data, speed time to market, diminish the costs of ownership, and a lot more that could take the businesses to new heights.
For example, the technology Intel has been offering has virtually evolved “off-the-shelf”, so competent than ever before that it can now solve specific infrastructure or business problems.
With the embracement of FPGA latest technology and eASICs(the Intel tech discussed above), there has been an acceleration in infrastructure use cases (SmartNICs). So, what is SmartNICs? Well, SmartNICs is a programmable accelerator. It holds the capability to centre all the networking data with the utmost security and proffering storage flexibility and efficiency at the same time.
Having SmartNIC onboard businesses hold enough power to handle more refined infrastructure workloads using cloud hosts, churning of wastage of time, and saving more resources. Besides, SmartNICs also furnish great value towards nurturing virtualized assistance, such as multi-tenant shared cloud and more.
Perhaps with hyper-scalers’ mushrooming, the overhead of network infrastructure might turn daunting. But the applications of FPGA have helped manage that.
Apart from this, Intel has also come up with FPGA cloud SmartNIC platforms that replicate the hyper-scalers’ used architectures. So, how does it operate?
This platform integrates Intel high-performance Stratix 10 FPGA with an Intel Xeon D processor that works together on the SmartNIC card, enabling virtual switching by offering the Tier-2 data centres a mass-market solution.
Intel has also been heavily sponsoring more efficient AI via recommender systems and natural language processing. It has even established a more robust form of FPGA, which is able to interpret voice coder inputs.
Jim contends that the enactment of a GPU manages to be modal and established on the micro-architecture constructed around it, irrespective of their power. Therefore if it shifts from an optimization point, latencies might rise, negatively impacting the performance of speech processing.
FPGA applications are virtually inexhaustible, particularly with FPGA transition reaching up to par with software programming in ease of usage.
Jim is optimistic about exploding evolution. He believes people wouldn’t be asking what SmartNIC platforms are. Instead would be keener towards knowing how transformative it could be. But if you ask me, I would still say the real excitement lies in accessing Intel’s technology and then jumping to Microsoft Azure to revise and enjoy leaner and faster service completely.
With his extensive product knowledge of large-scale integration work, Jim puts it; we must decode problems at a strategic level and not in a microcosm.
A New Breeding Ground For AI Apps, At The Intersection Of Knowledge And Scarcity
Artificial Intelligence bestows you the ability to scale wisdom. By corresponding the knowledge required to accomplish a chore against the absence of resources to execute it, you conspire with ample prospects for AI. Further, with the abundant availability of data and shortage of human resources to analyse them, AI is putting up a stage of opportunities for all.
Lately, I have had a great opportunity to interact with the IoT Marketing Global Lead for Intel, Alexis Crowell. And we conversed about challenges and prospects round and about AI adoption and how service issues are really assisting drive the knowledge-based AI forward.
Prospects and Challenges
Currently, Intel is at a position that shows a prospect of having 75% of AI onboard soon, fetching more horsepower to the juncture of data creation. However, what Alexis said was also worth noting that it isn’t feasible to appoint people to analyse such a high amount of data. In fact, the ratio of data output and people available aren’t corresponding.
To be honest, you can practically heap the challenges around knowledge base artificial intelligence edge elaboration with the drivers controlling the computing models. Therefore, the very things making the edge so desirable are also the cause of the difficulty. And that’s:
Web needs & mounting bandwidth
Scarcity of actionable insights
Machines breeding extensive data device multiplication aviating
Web tie-ups and latency
Fortunately, Intel holds a raft of use cases, having all successfully used knowledge-based AI at the edge, fetching solutions in this wilderness of extensive data.
While in retail, Tesco has been embracing Natural Language Processing (NLP) to provide accessible custom services for their consumers with knowledge base artificial intelligence. On the other hand, AI at airports has been helping keep traffic flowing safely.
With its enormous “brownfield” of present infrastructure, large-scale retrofits are challenging to make expense feasible in the automotive drive.
Likewise, edge computing knowledge-based AI use cases find abodes in the oil and gas enterprise, where anticipated supervision is mandated underwater, and negligence is fateful to the enterprise and environment.
Right now, even fast food is evolving AI use cases. For instance, one consumer Alexis talked about was sampling cloud-connected devices that can be remotely controlled, therefore decreasing the chore load on-site.
How Can You Make Data More Useful?
As the industries turn themselves into an AI breeding ground, the solution to what one is supposed to do with the accumulated data has also intensified. Alexis said that there is no specified key for the lock on this very note. He said the solution undoubtedly varies from customer to customer and use case, at least for now.
One excellent instance of creating more valuable data occurs at GE Healthcare, a long-time Intel associate. Healthcare is an untamed conjunction end because there are never sufficient medics or radiologists, particularly in the more rural environments. Employing AI at the edge can quicken time to diagnosis, which eventually enhances the grade and pace of comprehensive patient care.
Alexis believes the future darts an optimistic prospectus for AI at the edge. She says that one of the greatest understandings is that consumers ought to be obtaining the most out of their infrastructure.
Embracing Intel’s Xeon will directly avail you to fasten the process and possibly help absorb the new normal.
Join the entire podcast of my conversation with Alexis Crowell
Tune into the full podcast of my conversation with Alexis Crowell
Intel customer spotlight – Intel & GE Healthcare: https://intel.ly/3kA5K0Z
Intel customer spotlight – Intel & Audi Industry 4.0: https://intel.ly/2FQtWNH
AI analytics & Edge compute just accelerated, now what will innovators do with it?
Do not take the Intel portfolio for granted. Not for one second. Sure, Intel products are present everywhere in our digitalised world. But this company is way more than silicon, hardware, and software.
Not long ago, Intel introduced customisable silicon (such a win for their customers) and rapid-deployment options like Intel® Select Solutions pre-verified configurations of hardware and software. Now, the conversation has turned to the built-in AI acceleration on the newest 3rd Gen Intel® Xeon® Scalable processors; quite the incredible AI-infused, data-intensive digital solution.
Recently, I had the opportunity to talk with Lisa Spelman, Corporate Vice President, Data Platforms Group, General Manager Xeon and Memory Group at Intel Corporation who has an incredibly comprehensive view over the whole data-centric story. She is not only native to Portland, she’s also practically native to Intel, having worked for and managed dozens of different aspects of the company throughout her career. You’ll find a link at the end of this post to my podcast conversation with her.
Our conversation started off paying homage to the global Intel base. As a company, they recognised the gravity of the pandemic early on and immediately began work on high-performance computing infrastructure for genome sequencing of the virus, partnering with labs in China to get, as Lisa put it, a “head start on understanding what we’re dealing with.” Intel has since dug in, in every way possible, from a $50 USD million pandemic technology fund to helping virtual ICUs scale, to sponsoring employee innovations designed to combat the virus and protect communities.
And even though most of Intel’s 100,000+ employee base is still working from home, they’ve managed to launch new tech significant enough that it is changing the way organisations do business.
As Lisa explained, “When you look at the different challenges behind each use case, it comes down to, I have a movement challenge. I have a storage challenge. I have a processing challenge.” Technology should not be holding back enterprise, cloud service providers or network infrastructure customers. “We fundamentally see that every workload across that data pipeline is going to have artificial intelligence either built into the application or fundamentally as a cornerstone of the service being delivered by our customers.”
“We have to add artificial intelligence, acceleration and capabilities into our processor products.” She said. And so, they did. During the pandemic.
For the full launch details, follow the links at the end of the post. The highlights include:
● 3rd Gen Intel® Xeon® Scalable processors with built-in AI acceleration and bfloat16 instruction support
● Intel® Stratix® 10 FX FPGAs
● Next-generation high capacity SSDs with 3D NAND technology
● Intel® Distribution of OpenVINO™ toolkit
As Lisa was talking, I kept thinking about how we used to have to offload memory processing to faster components – and now, they’re bringing all that right back into the core platform. They don’t have to move the data on and off the chip to do the analytics. This is such a significant pivot, it’s going to change the way we approach data processing and analytics, particularly as we move toward the edge.
Lisa agrees. “Artificial intelligence as a component of workloads is becoming pervasive.” Even projects that aren’t AI-centric still have AI capability built-in to improve performance. So, “having your hardware foundation capable of artificial intelligence acceleration gives you fundamentally better performance and some future-proofing for the next couple of years,” Lisa says.
The launch itself is practically a portfolio – it’s not one thing or a cluster of things, it’s new capabilities, some of which get very specific, down to the sub-vertical. But the recurring theme is accelerating AI workloads, managing vast data quantities, and better enabling edge compute.
Built-in AI accelerated 3rd Gen Intel® Xeon® Scalable processors
Bfloat16 on the new 3rd Gen Xeon processors adjust the numerical format to provide the same ranges as FP32, but with half the bits, while maintaining similar accuracy in AI workloads. They cut the data movement and storage in half. And what it does for the Intel® Optane™ persistent memory? Twenty-five percent higher bandwidth for data analytics. Think about my other podcasts with Intel partners Siemens and Honeywell, where breakthroughs have happened on the prior gen Optane, and just imagine what will happen now.
Intel has enabled the 3rd Gen Xeon processor with artificial intelligence capabilities. To say this is a game changer is to grossly underestimate how much this will change the way we can now make everyday functions work. It also changes the who. Intel is enabling people without PhDs in AI or ML to leverage these capabilities; they just need to know how to call the APIs or use the libraries that Intel has built.
Now think about the “where.” “People sometimes mistakenly think of the edge as very low-end compute,” Lisa says. “But there’s a lot of pretty heavy compute happening at the edge… and we see opportunity for expansion.” Faster compute with lower latency and faster persistent memory will allow more processes to run with greater assurance of success. Things like autonomous vehicles that won’t get green-lit until they can pass safety tests with 9.9999 degrees of reliability.
Of course, the compute isn’t all moving to the edge – I see a fun challenge ahead of keeping enough intelligence at the edge where the devices and data are but at the same time bringing back relevant data where it can be used centrally by other systems and tools.
I want to thank Lisa, her team and all the folks at Intel for making this interview possible. Please tune in to the conversation at the link below and use the other resources to learn more about the technology and innovations we discussed.
- [#podcast] Conversations With Dez – with Lisa Spelman, VP & GM Xeon Products & Data Center Marketing, Intel
- Harness the Full Power of Your Data
- 3rd Gen Intel Xeon Scalable processors
- Intel Optane persistent memory
- Intel SSDs
- Intel Stratix 10 NX FPGA
- Intel Select Solutions
- [#podcast] Conversations with Dez: Tim Baker, Global Director of Marketing & Product Management of Commercial Security Products at Honeywell
- [#podcast] Conversations with Dez: Peter Shen, VP Business Development Digital Health, Siemens Healthineers
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